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- Publisher Website: 10.1109/TWC.2021.3065523
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Article: Resource Allocation in Uplink NOMA-IoT Networks: A Reinforcement-Learning Approach
Title | Resource Allocation in Uplink NOMA-IoT Networks: A Reinforcement-Learning Approach |
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Authors | |
Keywords | Deep reinforcement learning Internet of Things non-orthogonal multiple access power allocation SARSA learning user clustering |
Issue Date | 2021 |
Citation | IEEE Transactions on Wireless Communications, 2021, v. 20, n. 8, p. 5083-5098 How to Cite? |
Abstract | Non-orthogonal multiple access (NOMA) exploits the potential of the power domain to enhance the connectivity for the Internet of Things (IoT). Due to time-varying communication channels, dynamic user clustering is a promising method to increase the throughput of NOMA-IoT networks. This article develops an intelligent resource allocation scheme for uplink NOMA-IoT communications. To maximise the average performance of sum rates, this work designs an efficient optimization approach based on two reinforcement learning algorithms, namely deep reinforcement learning (DRL) and SARSA-learning. For light traffic, SARSA-learning is used to explore the safest resource allocation policy with low cost. For heavy traffic, DRL is used to handle traffic-introduced huge variables. With the aid of the considered approach, this work addresses two main problems of fair resource allocation in NOMA techniques: 1) allocating users dynamically and 2) balancing resource blocks and network traffic. We analytically demonstrate that the rate of convergence is inversely proportional to network sizes. Numerical results show that: 1) Compared with the optimal benchmark scheme, the proposed DRL and SARSA-learning algorithms have lower complexity with acceptable accuracy and 2) NOMA-enabled IoT networks outperform the conventional orthogonal multiple access based IoT networks in terms of system throughput. |
Persistent Identifier | http://hdl.handle.net/10722/349546 |
ISSN | 2023 Impact Factor: 8.9 2023 SCImago Journal Rankings: 5.371 |
DC Field | Value | Language |
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dc.contributor.author | Ahsan, Waleed | - |
dc.contributor.author | Yi, Wenqiang | - |
dc.contributor.author | Qin, Zhijin | - |
dc.contributor.author | Liu, Yuanwei | - |
dc.contributor.author | Nallanathan, Arumugam | - |
dc.date.accessioned | 2024-10-17T06:59:15Z | - |
dc.date.available | 2024-10-17T06:59:15Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | IEEE Transactions on Wireless Communications, 2021, v. 20, n. 8, p. 5083-5098 | - |
dc.identifier.issn | 1536-1276 | - |
dc.identifier.uri | http://hdl.handle.net/10722/349546 | - |
dc.description.abstract | Non-orthogonal multiple access (NOMA) exploits the potential of the power domain to enhance the connectivity for the Internet of Things (IoT). Due to time-varying communication channels, dynamic user clustering is a promising method to increase the throughput of NOMA-IoT networks. This article develops an intelligent resource allocation scheme for uplink NOMA-IoT communications. To maximise the average performance of sum rates, this work designs an efficient optimization approach based on two reinforcement learning algorithms, namely deep reinforcement learning (DRL) and SARSA-learning. For light traffic, SARSA-learning is used to explore the safest resource allocation policy with low cost. For heavy traffic, DRL is used to handle traffic-introduced huge variables. With the aid of the considered approach, this work addresses two main problems of fair resource allocation in NOMA techniques: 1) allocating users dynamically and 2) balancing resource blocks and network traffic. We analytically demonstrate that the rate of convergence is inversely proportional to network sizes. Numerical results show that: 1) Compared with the optimal benchmark scheme, the proposed DRL and SARSA-learning algorithms have lower complexity with acceptable accuracy and 2) NOMA-enabled IoT networks outperform the conventional orthogonal multiple access based IoT networks in terms of system throughput. | - |
dc.language | eng | - |
dc.relation.ispartof | IEEE Transactions on Wireless Communications | - |
dc.subject | Deep reinforcement learning | - |
dc.subject | Internet of Things | - |
dc.subject | non-orthogonal multiple access | - |
dc.subject | power allocation | - |
dc.subject | SARSA learning | - |
dc.subject | user clustering | - |
dc.title | Resource Allocation in Uplink NOMA-IoT Networks: A Reinforcement-Learning Approach | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/TWC.2021.3065523 | - |
dc.identifier.scopus | eid_2-s2.0-85103245472 | - |
dc.identifier.volume | 20 | - |
dc.identifier.issue | 8 | - |
dc.identifier.spage | 5083 | - |
dc.identifier.epage | 5098 | - |
dc.identifier.eissn | 1558-2248 | - |